Columbia University

research

∙ 09/08/2019

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

In recent years there has been an increasing trend in which data scienti...

Yaoli Mao, et al. ∙

research

∙ 05/11/2022

Deep Learning and Synthetic Media

Deep learning algorithms are rapidly changing the way in which audiovisu...

Raphaël Millière, et al. ∙

research

∙ 05/20/2021

DeepCAD: A Deep Generative Network for Computer-Aided Design Models

Deep generative models of 3D shapes have received a great deal of resear...

Rundi Wu, et al. ∙

research

∙ 10/08/2019

AutoML using Metadata Language Embeddings

As a human choosing a supervised learning algorithm, it is natural to be...

Iddo Drori, et al. ∙

research

∙ 12/19/2019

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax

The Gumbel-Softmax is a continuous distribution over the simplex that is...

Andres Potapczynski, et al. ∙

research

∙ 11/25/2019

One Man's Trash is Another Man's Treasure: Resisting Adversarial Examples by Adversarial Examples

Modern image classification systems are often built on deep neural netwo...

Chang Xiao, et al. ∙

research

∙ 04/10/2021

Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data

Current neuroimaging techniques provide paths to investigate the structu...

Nanyan Zhu, et al. ∙

research

∙ 05/06/2019

FaceShapeGene: A Disentangled Shape Representation for Flexible Face Image Editing

Existing methods for face image manipulation generally focus on editing ...

Sen-Zhe Xu, et al. ∙

research

∙ 12/12/2019

Theme-Matters: Fashion Compatibility Learning via Theme Attention

Fashion compatibility learning is important to many fashion markets such...

Jui-Hsin Lai, et al. ∙

research

∙ 07/16/2020

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

Parametric computer-aided design (CAD) is the dominant paradigm in mecha...

Ari Seff, et al. ∙

research

∙ 12/03/2019

Noise2Blur: Online Noise Extraction and Denoising

We propose a new framework called Noise2Blur (N2B) for training robust i...

Huangxing Lin, et al. ∙

research

∙ 02/06/2019

Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling

We consider the problem of inferring the values of an arbitrary set of v...

Hao Wang, et al. ∙

research

∙ 04/29/2022

Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics

The absence of a conventional association between the cell-cell cohabita...

Baihan Lin, et al. ∙

research

∙ 10/06/2019

Unrestricted Adversarial Attacks for Semantic Segmentation

Semantic segmentation is one of the most impactful applications of machi...

Guangyu Shen, et al. ∙

research

∙ 04/03/2019

Stacked Semantic-Guided Network for Zero-Shot Sketch-Based Image Retrieval

Zero-shot sketch-based image retrieval (ZS-SBIR) is a task of cross-doma...

Hao Wang, et al. ∙

research

∙ 08/18/2021

SynFace: Face Recognition with Synthetic Data

With the recent success of deep neural networks, remarkable progress has...

Haibo Qiu, et al. ∙

research

∙ 01/13/2022

NLP in Human Rights Research – Extracting Knowledge Graphs About Police and Army Units and Their Commanders

In this working paper we explore the use of an NLP system to assist the ...

Daniel Bauer, et al. ∙

research

∙ 09/20/2019

Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation

We present a robot navigation system that uses an imitation learning fra...

David Watkins-Valls, et al. ∙

research

∙ 04/22/2021

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

We present a framework for learning multimodal representations from unla...

Hassan Akbari, et al. ∙

research

∙ 12/05/2018

Learning to Compose Dynamic Tree Structures for Visual Contexts

We propose to compose dynamic tree structures that place the objects in ...

Kaihua Tang, et al. ∙

research

∙ 09/30/2019

Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health

In many review classification applications, a fine-grained analysis of t...

Giannis Karamanolakis, et al. ∙

research

∙ 10/04/2019

Zero Shot Learning on Simulated Robots

In this work we present a method for leveraging data from one source to ...

Robert Kwiatkowski, et al. ∙

research

∙ 11/21/2019

Controversial stimuli: pitting neural networks against each other as models of human recognition

Distinct scientific theories can make similar predictions. To adjudicate...

Tal Golan, et al. ∙

research

∙ 07/01/2020

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

To combat COVID-19, both clinicians and scientists need to digest the va...

Qingyun Wang, et al. ∙

research

∙ 03/21/2021

The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation

Identifying hidden dynamics from observed data is a significant and chal...

Qiang Du, et al. ∙

research

∙ 09/03/2021

Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning

Computer vision and machine learning tools offer an exciting new way for...

Maarja Bussov, et al. ∙

research

∙ 06/15/2022

Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook

Knowledge management systems are in high demand for industrial researche...

Baihan Lin, et al. ∙

research

∙ 06/04/2020

Towards Understanding Fast Adversarial Training

Current neural-network-based classifiers are susceptible to adversarial ...

Bai Li, et al. ∙

research

∙ 11/19/2018

Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. How...

Zhuoran Xiong, et al. ∙

research

∙ 01/28/2019

TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

Deep generative models have been successfully applied to many applicatio...

Zihan Ding, et al. ∙

research

∙ 04/03/2019

Unsupervised Deep Tracking

We propose an unsupervised visual tracking method in this paper. Differe...

Ning Wang, et al. ∙

research

∙ 09/25/2019

ES-MAML: Simple Hessian-Free Meta Learning

We introduce ES-MAML, a new framework for solving the model agnostic met...

Xingyou Song, et al. ∙

research

∙ 03/26/2021

Adversarial Attacks are Reversible with Natural Supervision

We find that images contain intrinsic structure that enables the reversa...

Chengzhi Mao, et al. ∙

research

∙ 06/28/2022

Reconstructing the Universe with Variational self-Boosted Sampling

Forward modeling approaches in cosmology have made it possible to recons...

Chirag Modi, et al. ∙

research

∙ 09/26/2021

Automated Multi-Process CTC Detection using Deep Learning

Circulating Tumor Cells (CTCs) bear great promise as biomarkers in tumor...

Elena Ivanova, et al. ∙

research

∙ 01/15/2020

Segmentation with Residual Attention U-Net and an Edge-Enhancement Approach Preserves Cell Shape Features

The ability to extrapolate gene expression dynamics in living single cel...

Nanyan Zhu, et al. ∙

research

∙ 09/14/2021

Rationales for Sequential Predictions

Sequence models are a critical component of modern NLP systems, but thei...

Keyon Vafa, et al. ∙

research

∙ 06/12/2022

A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Algorithms designed for single-agent reinforcement learning (RL) general...

Samuel Sokota, et al. ∙

research

∙ 06/16/2020

AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are formulated as minimax game pr...

Yuesong Tian, et al. ∙

research

∙ 03/28/2021

InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?

Sentence insertion is a delicate but fundamental NLP problem. Current ap...

Fang Wu, et al. ∙

research

∙ 03/07/2019

Adaptive Sample-Efficient Blackbox Optimization via ES-active Subspaces

We present a new algorithm ASEBO for conducting optimization of high-dim...

Krzysztof Choromanski, et al. ∙

research

∙ 07/30/2019

PointHop: An Explainable Machine Learning Method for Point Cloud Classification

An explainable machine learning method for point cloud classification, c...

Min Zhang, et al. ∙

research

∙ 08/27/2019

Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics

We present an end-to-end statistical framework for personalized, accurat...

Iñigo Urteaga, et al. ∙

research

∙ 02/13/2019

Rethinking Generative Coverage: A Pointwise Guaranteed Approach

All generative models have to combat missing modes. The conventional wis...

Peilin Zhong, et al. ∙

research

∙ 12/08/2019

Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging

We reproduced the results of CheXNet with fixed hyperparameters and 50 d...

John R. Zech, et al. ∙

research

∙ 06/24/2020

AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types

Can one build a knowledge graph (KG) for all products in the world? Know...

Xin Luna Dong, et al. ∙

research

∙ 01/05/2020

General Partial Label Learning via Dual Bipartite Graph Autoencoder

We formulate a practical yet challenging problem: General Partial Label ...

Brian Chen, et al. ∙

research

∙ 11/01/2018

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition

In this paper, we aim to address the problem of human interaction recogn...

Xiangbo Shu, et al. ∙

research

∙ 12/04/2018

Moment Matching for Multi-Source Domain Adaptation

Conventional unsupervised domain adaptation (UDA) assumes that training ...

Xingchao Peng, et al. ∙

research

∙ 02/17/2019

Fully-Featured Attribute Transfer

Image attribute transfer aims to change an input image to a target one w...

De Xie, et al. ∙

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